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<div><div class="header">
  <div class="headertitle"><div class="title">CMSIS-Stream </div></div>
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<div class="contents">
<div class="textblock"><p><a class="anchor" id="mainpage"></a> <b>CMSIS-Stream</b> is a Python package that provides methods, interfaces, and tools to optimize data block streaming between processing steps of a DSP/ML application.</p>
<h1><a class="anchor" id="autotoc_md0"></a>
Access to CMSIS-Stream</h1>
<ul>
<li><a href="https://github.com/ARM-software/cmsis-stream"><b>CMSIS-Stream GitHub Repo</b></a> - provides templates, usage examples, and access to documentation.</li>
<li><a href="https://pypi.org/project/cmsis-stream/"><b>CMSIS-Stream Python package</b></a> - provides easy access to tools via standard Python delivery system.</li>
</ul>
<h1><a class="anchor" id="autotoc_md1"></a>
Benefits</h1>
<ul>
<li>CMSIS-Stream enables a modular design that makes it easier to develop and maintain DSP pipelines.</li>
<li>The provided tools generate at build time an optimized scheduling of the processing nodes with minimized memory overhead.</li>
<li>Creates an easy to understand representation of the design visualized with a compute graph.</li>
</ul>
<h1><a class="anchor" id="autotoc_md2"></a>
Components</h1>
<p>The components of CMSIS-Stream are:</p>
<ul>
<li><b>Compute Graph</b> is the decomposition of the application in a directed graph that shows the data flow between processing nodes. It describes the data formats, FIFO buffers, data streams, and processing steps using a Python script file.</li>
<li><b>Tools</b> convert the Compute Graph with Python into processing steps at build-time with optimized schedule and memory usage.</li>
<li><b>Interfaces</b> header files, templates, and methods for data management (that works also on AMP systems).</li>
<li><b>Usage Examples</b> that help a software developer to get started.</li>
</ul>
<h1><a class="anchor" id="autotoc_md3"></a>
Example Compute Graph</h1>
<p>The diagram below shows a Compute Graph with four nodes:</p>
<ul>
<li>A source generating 5 values in with data type <code>q15</code> each time it is run.</li>
<li>A processing node (Filter) consuming and producing 7 values with data type <code>q15</code> each time it is run.</li>
<li>A processing node (Detector) consuming 5 values with data type <code>q15</code> and producing 2 values with data type <code>f32</code> each it is run.</li>
<li>A sink consuming 10 values with data type <code>f32</code> each time it is run.</li>
</ul>
<div class="image">
<img src="Compute-Graph-Sample.png" alt=""/>
<div class="caption">
Sample Compute Graph</div></div>
    <p>This Compute Graph is described with a Python script file that defines the nodes and their connections. This Python script computes a C++ implementation with static schedule and correctly sized memory buffers between each node. Nodes that are implemented in C are integrated using C++ wrappers.</p>
<h1><a class="anchor" id="autotoc_md4"></a>
Complex DSP Pipelines</h1>
<p>CMSIS-Stream gives the developer a toolbox that allows to create optimized DSP pipelines, that are for example required to optimize machine learning (ML) software stacks. With a Compute Graph complex DSP pipelines like shown in the diagram below can be described.</p>
<p>By optimizing signal conditioning and feature extraction, the complexity of the ML classifier. More DSP pre-processing helps therefore lowering the overall performance that is required for a ML application.</p>
<div class="image">
<img src="ML-Stack.png" alt=""/>
<div class="caption">
Example Machine Learning Stack</div></div>
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